Collective Operation Management In A Parallel Computer

ABSTRACT

Methods, apparatuses, and computer program products for collective operation management in a parallel computer are provided. Embodiments include a parallel computer having a plurality of compute nodes coupled for data communications over a data communications network. Embodiments include a first compute node entering a collective operation. Each compute node of the plurality of compute nodes is associated with the collective operation. In response to entering the collective operation, the first compute node decreases power consumption of the first compute node.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 13/795,213, filed on Mar. 12,2013.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatuses, and computer program products for collectiveoperation management in a parallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Current parallel computers are running a number of processes that just afew years ago would have seemed unimaginable. For example, modernparallel computer can execute millions of processes simultaneously. Withso many processes executing, the number of available geometries forcollective operations and algorithms for collective operations areextremely large. Selecting an appropriate geometry, selecting anoptimized algorithm, and performing other administrative tasksassociated with executing a collective operation can be resourceintensive.

SUMMARY OF THE INVENTION

Methods, apparatuses, and computer program products for collectiveoperation management in a parallel computer are provided. Embodimentsinclude a parallel computer having a plurality of compute nodes coupledfor data communications over a data communications network. Embodimentsalso include a first compute node of a plurality of compute nodesentering a collective operation. Each compute node of the plurality ofcompute nodes is associated with the collective operation. In responseto entering the collective operation, the first compute node decreasespower consumption of the first compute node.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of example embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of example embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system for collective operation managementin a parallel computer according to embodiments of the presentinvention.

FIG. 2 sets forth a block diagram of an example compute node useful in acollective operation management in a parallel computer according toembodiments of the present invention.

FIG. 3A sets forth a block diagram of an example Point-To-Point Adapteruseful in systems for collective operation management in a parallelcomputer according to embodiments of the present invention.

FIG. 3B sets forth a block diagram of an example Global CombiningNetwork Adapter useful in systems for collective operation management ina parallel computer according to embodiments of the present invention.

FIG. 4 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful insystems capable of collective operation management in a parallelcomputer according to embodiments of the present invention.

FIG. 5 sets forth a line drawing illustrating an example globalcombining network useful in systems capable of collective operationmanagement in a parallel computer according to embodiments of thepresent invention.

FIG. 6 sets forth a flow chart illustrating an example method forcollective operation management in a parallel computer according toembodiments of the present invention.

FIG. 7 sets forth a flow chart illustrating an additional example methodfor collective operation management in a parallel computer according toembodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating an additional example methodfor collective operation management in a parallel computer according toembodiments of the present invention.

FIG. 9 sets forth a flow chart illustrating an additional example methodfor collective operation management in a parallel computer according toembodiments of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Example methods, apparatuses, and computer program products forcollective operation management in a parallel computer in accordancewith the present invention are described with reference to theaccompanying drawings, beginning with FIG. 1. FIG. 1 illustrates anexample system for collective operation management in a parallelcomputer according to embodiments of the present invention. The systemof FIG. 1 includes a parallel computer (100), non-volatile memory forthe computer in the form of a data storage device (118), an outputdevice for the computer in the form of a printer (120), and aninput/output device for the computer in the form of a computer terminal(122).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a global combining network (106)which is optimized for collective operations using a binary tree networktopology, and a point-to-point network (108), which is optimized forpoint-to-point operations using a torus network topology. The globalcombining network (106) is a data communications network that includesdata communications links connected to the compute nodes (102) so as toorganize the compute nodes (102) as a binary tree. Each datacommunications network is implemented with data communications linksamong the compute nodes (102). The data communications links providedata communications for parallel operations among the compute nodes(102) of the parallel computer (100).

The compute nodes (102) of the parallel computer (100) are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on the parallel computer (100). Eachoperational group (132) of compute nodes is the set of compute nodesupon which a collective parallel operation executes. Each compute nodein the operational group (132) is assigned a unique rank that identifiesthe particular compute node in the operational group (132). Collectiveoperations are implemented with data communications among the computenodes of an operational group. Collective operations are those functionsthat involve all the compute nodes of an operational group (132). Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group (132) ofcompute nodes. Such an operational group (132) may include all thecompute nodes (102) in a parallel computer (100) or a subset all thecompute nodes (102). Collective operations are often built aroundpoint-to-point operations. A collective operation requires that allprocesses on all compute nodes within an operational group (132) callthe same collective operation with matching arguments. A ‘broadcast’ isan example of a collective operation for moving data among compute nodesof an operational group. A ‘reduce’ operation is an example of acollective operation that executes arithmetic or logical functions ondata distributed among the compute nodes of an operational group (132).An operational group (132) may be implemented as, for example, an MPI‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for use insystems configured according to embodiments of the present inventioninclude MPI and the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM wasdeveloped by the University of Tennessee, The Oak Ridge NationalLaboratory and Emory University. MPI is promulgated by the MPI Forum, anopen group with representatives from many organizations that define andmaintain the MPI standard. MPI at the time of this writing is a de factostandard for communication among compute nodes running a parallelprogram on a distributed memory parallel computer. This specificationsometimes uses MPI terminology for ease of explanation, although the useof MPI as such is not a requirement or limitation of the presentinvention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group(132). For example, in a ‘broadcast’ collective operation, the processon the compute node that distributes the data to all the other computenodes is an originating process. In a ‘gather’ operation, for example,the process on the compute node that received all the data from theother compute nodes is a receiving process. The compute node on whichsuch an originating or receiving process runs is referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. In a scatter operation, the logical root dividesdata on the root into segments and distributes a different segment toeach compute node in the operational group (132). In scatter operation,all processes typically specify the same receive count. The sendarguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer is divided and dispersed to all processes (including the processon the logical root). Each compute node is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0receives the first sendcount data elements from the send buffer. Rank 1receives the second sendcount data elements from the send buffer, and soon.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root compute node.

A reduction operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the receive buffer of a rootprocess. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following predefinedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes (102) inthe parallel computer (100) may be partitioned into processing sets suchthat each compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer (102). For example, in some configurations, eachprocessing set may be composed of eight compute nodes and one I/O node.In some other configurations, each processing set may be composed ofsixty-four compute nodes and one I/O node. Such example are forexplanation only, however, and not for limitation. Each I/O nodeprovides I/O services between compute nodes (102) of its processing setand a set of I/O devices. In the example of FIG. 1, the I/O nodes (110,114) are connected for data communications I/O devices (118, 120, 122)through local area network (‘LAN’) (130) implemented using high-speedEthernet.

The parallel computer (100) of FIG. 1 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the compute nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The parallel computer (100) of FIG. 1 operates generally for collectiveoperation management, the parallel computer including a plurality ofcompute nodes (100). Such a parallel computer (100) is typicallycomposed of many compute nodes, but for ease of explanation one of thecompute nodes (102 a) in this example are referenced in particular. Thecompute node (102 a) includes a management module (199) for collectiveoperation management in a parallel computer, the parallel computerincluding a plurality of compute nodes (100). The management module(199) of FIG. 1 may be embodied as a module of computer programinstructions executing on computer hardware. The management module (199)of FIG. 1 can implement collective operation management in a parallelcomputer (100) by: entering a collective operation and in response toentering the collective operation, decreasing power consumption of thefirst compute node.

The arrangement of nodes, networks, and I/O devices making up theexample apparatus illustrated in FIG. 1 are for explanation only, notfor limitation of the present invention. Systems configured forcollective operation management in a parallel computer according toembodiments of the present invention may include additional nodes,networks, devices, and architectures, not shown in FIG. 1, as will occurto those of skill in the art. The parallel computer (100) in the exampleof FIG. 1 includes sixteen compute nodes (102). Parallel computers (102)configured for collective operation management, according to embodimentsof the present invention, sometimes include thousands of compute nodes.In addition to Ethernet (174) and JTAG (104), networks in such dataprocessing systems may support many data communications protocolsincluding for example TCP (Transmission Control Protocol), IP (InternetProtocol), and others as will occur to those of skill in the art.Various embodiments of the present invention may be implemented on avariety of hardware platforms in addition to those illustrated in FIG.1.

Collective operation management in a parallel computer according toembodiments of the present invention is generally implemented on aparallel computer that includes a plurality of compute nodes organizedfor collective operations through at least one data communicationsnetwork. In fact, such computers may include thousands of such computenodes. Each compute node is in turn itself a kind of computer composedof one or more computer processing cores, its own computer memory, andits own input/output adapters. For further explanation, therefore, FIG.2 sets forth a block diagram of an example compute node (102) useful incollective operation management in a parallel computer according toembodiments of the present invention. The compute node (102) of FIG. 2includes a plurality of processing cores (165) as well as RAM (156). Theprocessing cores (165) of FIG. 2 may be configured on one or moreintegrated circuit dies. Processing cores (165) are connected to RAM(156) through a high-speed memory bus (155) and through a bus adapter(194) and an extension bus (168) to other components of the computenode.

Stored in RAM (156) is a parallel communications library (161), alibrary of computer program instructions that carry out parallelcommunications among compute nodes, including point-to-point operationsas well as collective operations. A library of parallel communicationsroutines may be developed from scratch for use in systems according toembodiments of the present invention, using a traditional programminglanguage such as the C programming language, and using traditionalprogramming methods to write parallel communications routines that sendand receive data among nodes on two independent data communicationsnetworks. Alternatively, existing prior art libraries may be improved tooperate according to embodiments of the present invention. Examples ofprior-art parallel communications libraries include the MPI library andthe ‘Parallel Virtual Machine’ (‘PVM’) library.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for theparallel communications library in a compute node of a parallel computerto run a single thread of execution with no user login and no securityissues because the thread is entitled to complete access to allresources of the node. The quantity and complexity of tasks to beperformed by an operating system on a compute node in a parallelcomputer therefore are smaller and less complex than those of anoperating system on a serial computer with many threads runningsimultaneously. In addition, there is no video I/O on the compute node(102) of FIG. 2, another factor that decreases the demands on theoperating system. The operating system (162) may therefore be quitelightweight by comparison with operating systems of general purposecomputers, a pared down version as it were, or an operating systemdeveloped specifically for operations on a particular parallel computer.Operating systems that may usefully be improved, simplified, for use ina compute node include UNIX™, Linux™, Windows XP™, AIX™, IBM's i5/OS™,and others as will occur to those of skill in the art.

Also stored in RAM (156) is a management module (199) for use incollective operation management in a parallel computer (100). Themanagement module (199) of FIG. 2 includes computer program instructionsthat, when executed, can be used for collective operation management ina parallel computer (100). Specifically, a management module on a rootnode may be configured to entering a collective operation and inresponse to entering the collective operation, decreasing powerconsumption of the first compute node.

The example compute node (102) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in apparatus useful for collectiveoperation management in a parallel computer include modems for wiredcommunications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (102)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 include a JTAGSlave circuit (176) that couples example compute node (102) for datacommunications to a JTAG Master circuit (178). JTAG is the usual nameused for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also useful asa mechanism for debugging embedded systems, providing a convenientalternative access point into the system. The example compute node ofFIG. 2 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processing core, itsown memory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processing core registers andmemory in compute node (102) for use in dynamically reassigning aconnected node to a block of compute nodes useful in systems forcollective operation management in a parallel computer according toembodiments of the present invention.

The data communications adapters in the example of FIG. 2 include aPoint-To-Point Network Adapter (180) that couples example compute node(102) for data communications to a network (108) for point-to-pointmessage passing operations such as, for example, a network configured asa three-dimensional torus or mesh. The Point-To-Point Adapter (180)provides data communications in six directions on three communicationsaxes, x, y, and z, through six bidirectional links: +x (181), −x (182),+y (183), −y (184), +z (185), and −z (186).

The data communications adapters in the example of FIG. 2 include aGlobal Combining Network Adapter (188) that couples example compute node(102) for data communications to a global combining network (106) forcollective message passing operations such as, for example, a networkconfigured as a binary tree. The Global Combining Network Adapter (188)provides data communications through three bidirectional links for eachglobal combining network (106) that the Global Combining Network Adapter(188) supports. In the example of FIG. 2, the Global Combining NetworkAdapter (188) provides data communications through three bidirectionallinks for global combining network (106): two to children nodes (190)and one to a parent node (192).

The example compute node (102) includes multiple arithmetic logic units(‘ALUs’). Each processing core (165) includes an ALU (166), and aseparate ALU (170) is dedicated to the exclusive use of the GlobalCombining Network Adapter (188) for use in performing the arithmetic andlogical functions of reduction operations, including an allreduceoperation. Computer program instructions of a reduction routine in aparallel communications library (161) may latch an instruction for anarithmetic or logical function into an instruction register (169). Whenthe arithmetic or logical function of a reduction operation is a ‘sum’or a ‘logical OR,’ for example, the collective operations adapter (188)may execute the arithmetic or logical operation by use of the ALU (166)in the processing core (165) or, typically much faster, by use of thededicated ALU (170) using data provided by the nodes (190, 192) on theglobal combining network (106) and data provided by processing cores(165) on the compute node (102).

Often when performing arithmetic operations in the global combiningnetwork adapter (188), however, the global combining network adapter(188) only serves to combine data received from the children nodes (190)and pass the result up the network (106) to the parent node (192).Similarly, the global combining network adapter (188) may only serve totransmit data received from the parent node (192) and pass the data downthe network (106) to the children nodes (190). That is, none of theprocessing cores (165) on the compute node (102) contribute data thatalters the output of ALU (170), which is then passed up or down theglobal combining network (106). Because the ALU (170) typically does notoutput any data onto the network (106) until the ALU (170) receivesinput from one of the processing cores (165), a processing core (165)may inject the identity element into the dedicated ALU (170) for theparticular arithmetic operation being perform in the ALU (170) in orderto prevent alteration of the output of the ALU (170). Injecting theidentity element into the ALU, however, often consumes numerousprocessing cycles. To further enhance performance in such cases, theexample compute node (102) includes dedicated hardware (171) forinjecting identity elements into the ALU (170) to reduce the amount ofprocessing core resources required to prevent alteration of the ALUoutput. The dedicated hardware (171) injects an identity element thatcorresponds to the particular arithmetic operation performed by the ALU.For example, when the global combining network adapter (188) performs abitwise OR on the data received from the children nodes (190), dedicatedhardware (171) may inject zeros into the ALU (170) to improveperformance throughout the global combining network (106).

For further explanation, FIG. 3A sets forth a block diagram of anexample Point-To-Point Adapter (180) useful in systems for collectiveoperation management in a parallel computer according to embodiments ofthe present invention. The Point-To-Point Adapter (180) is designed foruse in a data communications network optimized for point-to-pointoperations, a network that organizes compute nodes in athree-dimensional torus or mesh. The Point-To-Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). The Point-To-Point Adapter (180) of FIG. 3A alsoprovides data communication along a y-axis through four unidirectionaldata communications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). ThePoint-To-Point Adapter (180) of FIG. 3A also provides data communicationalong a z-axis through four unidirectional data communications links, toand from the next node in the −z direction (186) and to and from thenext node in the +z direction (185).

For further explanation, FIG. 3B sets forth a block diagram of anexample Global Combining Network Adapter (188) useful in systems forcollective operation management in a parallel computer according toembodiments of the present invention. The Global Combining NetworkAdapter (188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. The Global Combining Network Adapter (188) inthe example of FIG. 3B provides data communication to and from childrennodes of a global combining network through four unidirectional datacommunications links (190), and also provides data communication to andfrom a parent node of the global combining network through twounidirectional data communications links (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in systems capable of collectiveoperation management in a parallel computer according to embodiments ofthe present invention. In the example of FIG. 4, dots represent computenodes (102) of a parallel computer, and the dotted lines between thedots represent data communications links (103) between compute nodes.The data communications links are implemented with point-to-point datacommunications adapters similar to the one illustrated for example inFIG. 3A, with data communications links on three axis, x, y, and z, andto and from in six directions +x (181), −x (182), +y (183), −y (184), +z(185), and −z (186). The links and compute nodes are organized by thisdata communications network optimized for point-to-point operations intoa three dimensional mesh (105). The mesh (105) has wrap-around links oneach axis that connect the outermost compute nodes in the mesh (105) onopposite sides of the mesh (105). These wrap-around links form a torus(107). Each compute node in the torus has a location in the torus thatis uniquely specified by a set of x, y, z coordinates. Readers will notethat the wrap-around links in the y and z directions have been omittedfor clarity, but are configured in a similar manner to the wrap-aroundlink illustrated in the x direction. For clarity of explanation, thedata communications network of FIG. 4 is illustrated with only 27compute nodes, but readers will recognize that a data communicationsnetwork optimized for point-to-point operations for use in collectiveoperation management in a parallel computer in accordance withembodiments of the present invention may contain only a few computenodes or may contain thousands of compute nodes. For ease ofexplanation, the data communications network of FIG. 4 is illustratedwith only three dimensions, but readers will recognize that a datacommunications network optimized for point-to-point operations for usein collective operation management in a parallel computer in accordancewith embodiments of the present invention may in fact be implemented intwo dimensions, four dimensions, five dimensions, and so on. Severalsupercomputers now use five dimensional mesh or torus networks,including, for example, IBM's Blue Gene Q™.

For further explanation, FIG. 5 sets forth a line drawing illustratingan example global combining network (106) useful in systems capable ofcollective operation management in a parallel computer according toembodiments of the present invention. The example data communicationsnetwork of FIG. 5 includes data communications links (103) connected tothe compute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with global combining network adapters similar tothe one illustrated for example in FIG. 3B, with each node typicallyproviding data communications to and from two children nodes and datacommunications to and from a parent node, with some exceptions. Nodes inthe global combining network (106) may be characterized as a physicalroot compute node (202), branch nodes (204), and leaf nodes (206). Thephysical root (202) has two children but no parent and is so calledbecause the physical root compute node (202) is the node physicallyconfigured at the top of the binary tree. The leaf nodes (206) each hasa parent, but leaf nodes have no children. The branch nodes (204) eachhas both a parent and two children. The links and compute nodes arethereby organized by this data communications network optimized forcollective operations into a binary tree (106). For clarity ofexplanation, the data communications network of FIG. 5 is illustratedwith only 31 compute nodes, but readers will recognize that a globalcombining network (106) optimized for collective operations for use incollective operation management in a parallel computer in accordancewith embodiments of the present invention may contain only a few computenodes or may contain thousands of compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifies atask or process that is executing a parallel operation. Using the rankto identify a node assumes that only one such task is executing on eachnode. To the extent that more than one participating task executes on asingle node, the rank identifies the task as such rather than the node.A rank uniquely identifies a task's location in the tree network for usein both point-to-point and collective operations in the tree network.The ranks in this example are assigned as integers beginning with 0assigned to the root tasks or root compute node (202), 1 assigned to thefirst node in the second layer of the tree, 2 assigned to the secondnode in the second layer of the tree, 3 assigned to the first node inthe third layer of the tree, 4 assigned to the second node in the thirdlayer of the tree, and so on. For ease of illustration, only the ranksof the first three layers of the tree are shown here, but all computenodes in the tree network are assigned a unique rank.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method for collective operation management in a parallelcomputer (670) according to embodiments of the present invention. In theexample of FIG. 6, the parallel computer includes a plurality (674) ofcompute nodes coupled for data communications over a data communicationsnetwork and each compute node of the plurality of compute nodes (672) isassociated with the collective operation (650).

The method of FIG. 6 includes a first compute node (672) entering (602)a collective operation (650). Entering (602) a collective operation(650) may be carried out by reaching a particular execution point withina collective operation, for example, a starting barrier.

The method of FIG. 6 also includes the first compute node (672)decreasing (604) power consumption of the first compute node (672) inresponse to entering the collective operation (650). Decreasing (604)power consumption of the first compute node (672) in response toentering the collective operation (650) may be carried out bytransmitting a message to a service node instructing the service node toreduce power consumption of the first compute node. The service node mayreduce the power consumption of the first compute node by throttlingprocessor speed, throttling processor operating voltages, and throttlingmemory bus speeds.

The method of FIG. 6 also includes the first compute node (672),transmitting (606) to at least one of the other compute nodes of theplurality (674) of compute nodes, in response to entering the collectiveoperation (650), an active message (652) indicating that the firstcompute node (672) entered the collective operation (650). Transmitting(606) to at least one of the other compute nodes an active message (652)indicating that the first compute node (672) entered the collectiveoperation (650) may be carried out by identifying a plurality of computenodes in an operational group that is associated with a collectiveoperation; and sending a message to one or more of the identifiedcompute nodes.

For further explanation, FIG. 7 sets forth a flow chart illustratinganother example method for collective operation management in a parallelcomputer (670) according to embodiments of the present invention. Themethod of FIG. 7 is similar to the method of FIG. 6 in that the methodof FIG. 7 also includes a first compute node (672) entering (602) acollective operation (650); the first compute node (672) decreasing(604) power consumption of the first compute node (672) in response toentering the collective operation (650); and transmitting (606) to atleast one of the other compute nodes of the plurality (674) of computenodes, an active message (652) indicating that the first compute node(672) entered the collective operation (650).

In the method of FIG. 7, however, transmitting (606) to at least one ofthe other compute nodes, an active message (652) indicating that thefirst compute node (672) entered the collective operation (650) includesthe first compute node (674) transmitting (702) the active message (652)to each of the other compute node of the plurality (674) of computenodes. Transmitting (702) the active message (652) to each of the othercompute node of the plurality (674) of compute nodes may be carried outby identifying a plurality of compute nodes in an operational group thatis associated with a collective operation; and sending a message to eachof the identified compute nodes.

The method of FIG. 7 also includes a second compute node (772) of theplurality (674) of compute nodes receiving (704) the active message(652) from the first compute node (674). Receiving (704) the activemessage (652) from the first compute node (674) may be carried out byreceiving the active message via a data communications network.

The method of FIG. 7 also includes a second compute node (772)increasing (706), power consumption of the second compute node (772) inresponse to receiving the active message (652). Increasing (706), powerconsumption of the second compute node (772) in response to receivingthe active message (652) may be carried out by transmitting a message toa service node instructing the service node to increase powerconsumption of the compute node. The service node may increase the powerconsumption of a particular compute node by throttling-up the processorspeed of the compute node. That is, the service compute node mayallocate power amongst the compute nodes in the tree data communicationsnetwork so as to increase performance of the ‘slowest’ compute nodeswhile only marginally decreasing performance of the ‘fastest’ computenodes.

For further explanation, FIG. 8 sets forth a flow chart illustratinganother example method for collective operation management in a parallelcomputer (670) according to embodiments of the present invention. Themethod of FIG. 8 is similar to the method of FIG. 6 in that the methodof FIG. 8 also includes a first compute node (672) entering (602) acollective operation (650); the first compute node (672) decreasing(604) power consumption of the first compute node (672) in response toentering the collective operation (650); and transmitting (606) to atleast one of the other compute nodes of the plurality (674) of computenodes, an active message (652) indicating that the first compute node(672) entered the collective operation (650).

In the method of FIG. 8, however, transmitting (606) to at least one ofthe other compute nodes, an active message (652) indicating that thefirst compute node (672) entered the collective operation (650) includesthe first compute node (672) identifying (802) a set (850) of computenodes that have not entered the collective operation (650). Identifying(802) a set (850) of compute nodes that have not entered the collectiveoperation (650) may be carried out by determining which compute nodes ofthe plurality of computer nodes have entered the collective operation;and based on the determination, identifying the compute nodes that havenot entered the collective operation.

In the method of FIG. 8, however, transmitting (606) to at least one ofthe other compute nodes, an active message (652) indicating that thefirst compute node (672) entered the collective operation (650) includesthe first compute node (672) transmitting (804) the active message (652)to the identified set (850) of compute nodes. Transmitting (804) theactive message (652) to the identified set (850) of compute nodes may becarried out by sending a message via the data communications network toeach of the identified compute nodes.

The method of FIG. 8 also includes a second compute node (872) of theplurality (674) of compute nodes receiving (806) the active message(652) from the first compute node (672). Receiving (806) the activemessage (652) from the first compute node (672) may be carried out byreceiving the active message via a data communications network.

The method of FIG. 8 also includes the second compute node (872)determining (808) whether the second compute node (872) has entered thecollective operation (652). Determining (808) whether the second computenode (872) has entered the collective operation (652) may be carried outby keeping track of the status of the processor tasks including thestatus of the collective operation.

If the second compute node (872) has entered the collective operation(652), the method of FIG. 8 continues by the second compute node (872)disregarding (810) the received active message (652). Disregarding (810)the received active message (652) may be carried out by marking theactive message as no-action.

If the second compute node (872) has not entered the collectiveoperation (652), the method of FIG. 8 continues by increasing (812)power consumption of the second compute node (872). Increasing (812)power consumption of the second compute node (872) may be carried out bytransmitting a message to a service node instructing the service node toincrease power consumption of the child compute node. The service nodemay increase the power consumption of a particular compute node bythrottling-up the processor speed of the compute node. That is, theservice compute node may allocate power amongst the compute nodes in thetree data communications network so as to increase performance of the‘slowest’ compute nodes while only marginally decreasing performance ofthe ‘fastest’ compute nodes. In a particular embodiment, the secondcompute node (872) increases the power consumption of the second compute(872) by an amount that is based on a number of active messagesreceived.

For further explanation, FIG. 9 sets forth a flow chart illustratinganother example method for collective operation management in a parallelcomputer (670) according to embodiments of the present invention. Themethod of FIG. 9 is similar to the method of FIG. 6 in that the methodof FIG. 9 also includes a first compute node (672) entering (602) acollective operation (650); the first compute node (672) decreasing(604) power consumption of the first compute node (672) in response toentering the collective operation (650); and transmitting (606) to atleast one of the other compute nodes of the plurality (674) of computenodes, an active message (652) indicating that the first compute node(672) entered the collective operation (650).

The method of FIG. 9 includes the first compute node (672) releasing(902) a resource (960) assigned to the first compute node (672) inresponse to entering the collective operation (652). Releasing (902) aresource (960) assigned to the first compute node (672) in response toentering the collective operation (652) may be carried out by releasinga thread assigned to the first compute node.

The method of FIG. 9 also includes a second compute node (972) assuming(904) in response to receiving the active message, assignment of theresource (960) until the second compute node (972) enters the collectiveoperation (652). Assuming (904) in response to receiving the activemessage, assignment of the resource (960) until the second compute node(972) enters the collective operation (652) may be carried out bytransferring assignment of a processor thread to the second computenode; and assigning communication tokens.

Example embodiments of the present invention are described largely inthe context of a fully functional computer system for collectiveoperation management in a parallel computer. Readers of skill in the artwill recognize, however, that the present invention also may be embodiedin a computer program product disposed upon computer readable storagemedia for use with any suitable data processing system. Such computerreadable storage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, or other suitablemedia. Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodof the invention as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theexample embodiments described in this specification are oriented tosoftware installed and executing on computer hardware, nevertheless,alternative embodiments implemented as firmware or as hardware are wellwithin the scope of the present invention.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

1. A method of collective operation management in a parallel computer,the parallel computer including a plurality of compute nodes coupled fordata communications over a data communications network, the methodcomprising: entering, by a first compute node of the plurality ofcompute nodes, a collective operation, wherein each compute node of theplurality of compute nodes is associated with the collective operation;and decreasing, in response to entering the collective operation, by thefirst compute node, power consumption of the first compute node.
 2. Themethod of claim 1 further comprising in response to entering thecollective operation, by the first compute node, transmitting to atleast one of the other compute nodes of the plurality of compute nodes,an active message indicating that the first compute node entered thecollective operation.
 3. The method of claim 2 wherein transmitting toat least one of the other compute nodes of the plurality of computenodes, an active message indicating that the first compute node enteredthe collective operation includes transmitting the active message toeach of the other compute node of the plurality of compute nodes.
 4. Themethod of claim 3 further comprising: receiving, by a second computenode, the active message from the first compute node; and in response toreceiving the active message, increasing, by the second compute node,power consumption of the second compute node.
 5. The method of claim 2wherein transmitting to at least one of the other compute nodes of theplurality of compute nodes, an active message indicating that the firstcompute node entered the collective operation includes: identifying aset of compute nodes that have not entered the collective operation;transmitting the active message to the identified set of compute nodes.6. The method of claim 5 further comprising: receiving, by a secondcompute node, the active message from the first compute node;determining, by the second compute node, whether the second compute nodehas entered the collective operation; if the second compute node hasentered the collective operation, disregarding, by the second computenode, the received active message; and if the second compute node hasnot entered the collective operation, increasing, by the second computenode, power consumption of the second compute node.
 7. The method ofclaim 6 wherein the second compute node increases the power consumptionof the second compute by an amount that is based on a number of activemessages received.
 8. The method of claim 2 further comprising:releasing, in response to entering the collective operation, by thefirst compute node, a resource assigned to the first compute node; andin response to receiving the active message, assuming, by a secondcompute node, assignment of the resource until the second compute nodeenters the collective operation. 9-20. (canceled)